library(tidyverse) # for graphing and data cleaning
library(gardenR) # for Lisa's garden data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
theme_set(theme_minimal()) # My favorite ggplot() theme :)
# Lisa's garden data
data("garden_harvest")
# Seeds/plants (and other garden supply) costs
data("garden_spending")
# Planting dates and locations
data("garden_planting")
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(day = wday(date, label = TRUE)) %>%
group_by(vegetable, day) %>%
summarize(tot_harvest = sum(weight)*0.00220462) %>%
pivot_wider(id_cols = vegetable, names_from = day, values_from = tot_harvest)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?head(garden_planting)
garden_harvest %>%
group_by(variety) %>%
summarize(tot_harvest = sum(weight)*0.00220462) %>%
left_join(garden_planting, by = 'variety' )
Some of the vegetables are missing values on the planting data, this due mulitple reasons including that some of them were not planted. To solve the issue we could either delete of the NA values, given that these observations are not fundamental for our analysis.
garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.garden_harvest %>%
group_by(variety) %>%
summarize(tot_harvest = sum(weight)*0.00220462)
garden_spending
garden_planting%>%
group_by(variety) %>%
summarize(tot_seeds = sum(number_seeds_planted))
I would group the observations on the garden harvest data by variety and summarize their total weight in pounds. I would then left join the garden harvest data and the garden spending data. Then I would group by variety the garden planting data and summarize to show the total number of seeds per variety. Then I would left joing the new table with the other two. Then I would gruop the store data by variety and transform the names of the observations in order for R to match the varieties with their correct price per pound. Then I would left join it to the other tables. I would also create another variable called net costs that would multiply the amount of seeds used and their price plus taxes. After, I would create a new variable called gross savings that multiply the total harvest weight by the price from the data set. Then I would sum the values of the gross savings variable. And also separately sum the values of the net costs. Then I would find the difference between both to find the savings.
order_harvest <- garden_harvest %>%
filter(vegetable == "tomatoes") %>%
mutate(variety2 = fct_reorder(variety, date, .fun = min)) %>%
group_by(variety2) %>%
summarise(date, variety2,
weight,
tot_harvest = sum(weight)*0.00220462) %>%
distinct(variety2, tot_harvest)
order_harvest
order_harvest %>%
ggplot(aes(y = fct_rev(variety2), x = tot_harvest)) +
geom_col()+
labs(title = 'Tomato varieties in ascending order by first harvest',
x = "Total Harvest (lbs)",
y = "")
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(varlower = str_to_lower(variety),
lengthvar = str_length(variety)) %>%
group_by(vegetable) %>%
arrange(lengthvar) %>%
distinct(varlower)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
mutate(ar_er = str_detect(variety, "ar|er")) %>%
filter(ar_er == TRUE) %>%
distinct(variety)
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
{300px}
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Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot(aes(x = sdate)) +
geom_density()+
labs(title = "Events vs Date",
x = "",
y = "")
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute) %>%
ggplot(aes(x = time_day)) +
geom_density()+
labs(title = "Events vs Time of day",
x = "",
y = "")
Trips %>%
mutate(week_day = wday(sdate, label = TRUE)) %>%
ggplot(aes(y = week_day))+
geom_bar()+
labs(title = "Event vs Day of Week",
x = "",
y = "")
Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute,
week_day = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_day)) +
geom_density()+
facet_wrap(vars(week_day))+
labs(title = "Proportion of events vs Time of Day for each day of the week",
x = "",
y = "")
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute,
week_day = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_day, fill = client)) +
geom_density(alpha = 0.5, color = NA)+
facet_wrap(vars(week_day))+
labs(title = "Proportion of events vs Time of Day for each day of the week",
x = "",
y = "",
fill = "Type of client")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute,
week_day = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_day, fill = client )) +
geom_density(alpha = 0.5, color = NA, position = position_stack())+
facet_wrap(vars(week_day))+
labs(title = "Proportion of events vs Time of Day for each day of the week",
x = "",
y = "",
fill = "Type of client")
I think that this is better to tell the story, considering that with stacking we see how many times bigges the casual riders are relative to the registered riders on each day. A disadvantage is that we can’t see the proportions right away, for the casual riders, as we have to substract the registered proportions. The advange for the non-stacked is that we can see the proportions for each right away and the distadvantage is that it is harder to compare the sizes of both casual and registered riders.
position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute,
week_day = wday(sdate, label = TRUE),
weekend = ifelse(week_day %in% c("Sat","Sun"), "weekend", "weekday")) %>%
ggplot(aes(x = time_day, fill = client )) +
geom_density(alpha = 0.5, color = NA)+
facet_wrap(vars(weekend))+
labs(title = "Proportion of events vs Time of Day for weekends and weekdays",
x = "",
y = "",
fill = "Type of client")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate)*1/60,
time_day = hour+minute,
week_day = wday(sdate, label = TRUE),
weekend = ifelse(week_day %in% c("Sat","Sun"), "weekend", "weekday")) %>%
ggplot(aes(x = time_day, fill = weekend )) +
geom_density(alpha = 0.5, color = NA)+
facet_wrap(vars(client))+
labs(title = "Proportion of events vs Time of Day for each comsumer type",
x = "",
y = "",
fill = " ")
This graph tells us the difference in time distribution between weekends and weekdays for both casual and registered riders. Whether a graph is better or not depends on the question we are trying to answer. In this case, the graph would be better if we were interested on answering how weekend and week day ridership is different for casual and registered riders. The previous graph would’ve been better if the question was focused on the distribution of casual and registered riders across weekends and weekdays.
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
left_join(Stations, by = c('sstation' = 'name')) %>%
group_by(lat, long) %>%
summarize(count = n()) %>%
ggplot(aes(x = long, y = lat, color = count))+
geom_point()+
labs(x = 'Longitude',
y = 'Latitude',
color = '# of departures')
Trips %>%
left_join(Stations, by = c('sstation' = 'name')) %>%
group_by(lat, long) %>%
summarize(count = n(),
proportion_casual = mean(client == 'Casual') ) %>%
ggplot(aes(x = long, y = lat, color = proportion_casual))+
geom_point()+
labs(title = "Spacial distribution of bike stations",
x = 'Longitude',
y = 'Latitude',
color = "Casual clients'
Proportion")
I notice that there is a concentration of stations with a high proportion of casual riders in stations located between longitudes -77.1 and -77.
as_date(sdate) converts sdate from date-time format to date format.first_ten <- Trips %>%
mutate(date_format = as_date(sdate)) %>%
group_by(date_format,sstation) %>%
count() %>%
arrange(desc(n)) %>%
head(n = 10)
first_ten
new_table <- Trips %>%
mutate(date_format = as_date(sdate)) %>%
semi_join(first_ten,
by = c("sstation","date_format"))
head(new_table)
Trips %>%
mutate(date_format = as_date(sdate)) %>%
semi_join(first_ten,
by = c("sstation","date_format")) %>%
mutate(week_day = wday(sdate, label = TRUE)) %>%
group_by(client, week_day) %>%
count() %>%
group_by(client) %>%
mutate(prop_n = n/sum(n)) %>%
select(-n) %>%
pivot_wider(names_from = client, values_from = prop_n)
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?